Original research · 2026-07 edition

AI SEO Statistics: Preschool (2026-07 edition)

15 questions · 45 AI responses · 3 models · measured 2026-07-04

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The question bank

The questions we tested — sampled from real buyer journeys in preschool.

Each model answered every question once, same wording, same day. These are the prompts behind every percentage on this page.

How do I know if my 3-year-old is actually ready for preschool or if I should wait another year?
What are the biggest differences between Montessori and Reggio Emilia teaching styles for a toddler?
I'm starting a new job in three weeks and need a preschool spot fast; what's the best way to find openings quickly?
What specific questions should I ask a preschool director about their teacher turnover rate?
Is it better to choose a preschool close to my house or one closer to my office in case of emergencies?
What is a reasonable monthly tuition for a full-time private preschool in a mid-sized city?
Are there any red flags I should look for when I'm doing a physical walkthrough of a daycare or preschool facility?
What are the social benefits of preschool compared to just having a nanny at home?
Show all 15 questions
Do most preschools require children to be fully potty trained before they can enroll?
How can I check if a local preschool has had any recent safety violations or licensing issues?
Is it worth paying more for a preschool that offers a second language immersion program?
What does a typical daily schedule look like for a 4-year-old in a play-based curriculum?
My child has a severe peanut allergy; what kind of safety protocols should a high-quality preschool have in place?
How early do I realistically need to get on a waiting list for a top-rated preschool?
Are there any financial assistance programs or tax credits that help cover the cost of private preschool?

Model by model

19-point average divergence: which AI you ask changes the answer.

The divergence index is the average gap between the most and least likely model per behavior. Higher = the models disagree more about preschool buyers.

Behavior rates across 15 preschool buyer questions, 2026-07 edition. Last column: average across models.
ChatGPTClaudeGeminiConsensus
Recommends hiring a professional33%13%0%60%
Suggests DIY first27%27%13%67%
Names specific providers7%7%7%80%
Gives price or cost info13%13%13%100%
Tells to check reviews20%13%0%80%
Tells to verify credentials33%20%7%73%
Mentions case studies / portfolio0%0%0%100%
Mentions local proximity13%40%20%67%
Gives selection criteria60%60%33%40%
Warns about red flags27%20%20%80%
Asks a clarifying question47%73%0%20%
Recommends multiple quotes7%13%0%87%

By model

How each assistant handled Preschool questions.

Reading the 45 answers model by model shows how differently the three assistants treat the same preschool questions. On the most consequential behavior — whether to send the buyer to a professional at all — the rate ranged from 33.3% (ChatGPT) down to 0% (Gemini), a 33-point gap on an identical question set.

Across the 15 preschool answers it produced, ChatGPT recommended hiring a professional in 33.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 6.7% of answers (about 0 distinct providers per answer) and included price or cost information 13.3% of the time. ChatGPT asked a clarifying question before answering in 46.7% of cases, warned about red flags or scams in 26.7%, and told the buyer to verify credentials in 33.3%, averaging 557 words per answer. On the remaining cues it told the buyer to check reviews in 20%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 13.3%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 6.7%.

Across the 15 preschool answers it produced, Claude recommended hiring a professional in 13.3% of them and suggested a DIY approach first 26.7% of the time. It named a specific provider in 6.7% of answers (about 0.3 distinct providers per answer) and included price or cost information 13.3% of the time. Claude asked a clarifying question before answering in 73.3% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 20%, averaging 287 words per answer. On the remaining cues it told the buyer to check reviews in 13.3%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 40%; a selection-criteria checklist appeared in 60% of its answers and a recommendation to gather multiple quotes in 13.3%.

Across the 15 preschool answers it produced, Gemini recommended hiring a professional in 0% of them and suggested a DIY approach first 13.3% of the time. It named a specific provider in 6.7% of answers (about 0.2 distinct providers per answer) and included price or cost information 13.3% of the time. Gemini asked a clarifying question before answering in 0% of cases, warned about red flags or scams in 20%, and told the buyer to verify credentials in 6.7%, averaging 261 words per answer. On the remaining cues it told the buyer to check reviews in 0%, pointed to case studies or a portfolio in 0%, and framed the choice around local proximity in 20%; a selection-criteria checklist appeared in 33.3% of its answers and a recommendation to gather multiple quotes in 0%.

Taken together, ChatGPT is the assistant most likely to route a preschool buyer to a professional (33.3%) and Gemini the least (0%). ChatGPT produced the longest answers, at 557 words on average. Specific providers were named most often by ChatGPT (6.7%) — even there, roughly one answer in 15 carried a name.

Where they disagree

The behaviors where the choice of model changes the answer.

The divergence index for this study is 19.3 points — the average distance between the most and least likely model across the coded behaviors. The gaps below are where which assistant a preschool buyer happens to ask matters most:

  • Asks a clarifying question: from 0% (Gemini) to 73.3% (Claude) — a 73-point spread.
  • Recommends hiring a professional: from 0% (Gemini) to 33.3% (ChatGPT) — a 33-point spread.
  • Mentions local proximity: from 13.3% (ChatGPT) to 40% (Claude) — a 27-point spread.
  • Gives selection criteria: from 33.3% (Gemini) to 60% (ChatGPT) — a 27-point spread.
  • Tells the buyer to verify credentials: from 6.7% (Gemini) to 33.3% (ChatGPT) — a 27-point spread.

The widest single gap — asks a clarifying question, 73 points — means a preschool buyer can receive materially different guidance on the same question depending only on which assistant they happen to open, so any visibility strategy built on a single model's behavior describes only part of the preschool market.

Where they agree

The points of near-consensus in Preschool.

On other behaviors the three models move almost in lockstep — the points of near-consensus for preschool, where all three landed within a few points of each other:

  • Names a specific provider: 6.7% across all three models.
  • Gives price or cost information: 13.3% across all three models.
  • Mentions case studies or portfolio: 0% across all three models.
  • Warns about red flags or scams: 20%–26.7% across all three (a 7-point spread).

Measured question by question, the three assistants coded a response the same way most consistently on "gives price or cost information" (identical coding in 100% of questions) and least consistently on "asks a clarifying question" (20%).

Every behavior, measured

All twelve coded behaviors for Preschool, averaged across the three models.

The behaviors AI models reproduce most often for preschool are gives selection criteria (51.1% on average), asks a clarifying question (40%) and mentions local proximity (24.4%); the rarest are mentions case studies or portfolio (0%), recommends multiple quotes (6.7%) and names a specific provider (6.7%). Each figure below is the share of a model's 15 answers in which the behavior appeared at least once, averaged across the 3 models with the full per-model range in parentheses:

  • Gives selection criteria: 51.1% on average (ChatGPT 60%, Claude 60%, Gemini 33.3%) — a 27-point spread.
  • Asks a clarifying question: 40% on average (ChatGPT 46.7%, Claude 73.3%, Gemini 0%) — a 73-point spread.
  • Mentions local proximity: 24.4% on average (ChatGPT 13.3%, Claude 40%, Gemini 20%) — a 27-point spread.
  • Suggests a DIY approach first: 22.2% on average (ChatGPT 26.7%, Claude 26.7%, Gemini 13.3%) — a 13-point spread.
  • Warns about red flags or scams: 22.2% on average (ChatGPT 26.7%, Claude 20%, Gemini 20%) — a 7-point spread.
  • Tells the buyer to verify credentials: 20% on average (ChatGPT 33.3%, Claude 20%, Gemini 6.7%) — a 27-point spread.
  • Recommends hiring a professional: 15.5% on average (ChatGPT 33.3%, Claude 13.3%, Gemini 0%) — a 33-point spread.
  • Gives price or cost information: 13.3% on average (ChatGPT 13.3%, Claude 13.3%, Gemini 13.3%).
  • Tells the buyer to check reviews: 11.1% on average (ChatGPT 20%, Claude 13.3%, Gemini 0%) — a 20-point spread.
  • Names a specific provider: 6.7% on average (ChatGPT 6.7%, Claude 6.7%, Gemini 6.7%).
  • Recommends multiple quotes: 6.7% on average (ChatGPT 6.7%, Claude 13.3%, Gemini 0%) — a 13-point spread.
  • Mentions case studies or portfolio: 0% on average (ChatGPT 0%, Claude 0%, Gemini 0%).

Trust signals

How well the models protect the preschool buyer.

Beyond whether to hire, the rubric codes how carefully each assistant protects the preschool buyer once a decision is made. Telling the buyer to check reviews or ratings appeared in 11.1% of answers on average. Verifying credentials or certifications appeared in 20%. Warning about red flags or scams appeared in 22.2%.

On structuring the decision, a selection-criteria checklist showed up in 51.1% of answers on average and a recommendation to gather multiple quotes in 6.7%. The single least-reproduced protective signal for preschool is "recommends multiple quotes" at 6.7% on average — the clearest opening for content that supplies it, since the models are not yet reliably surfacing that guidance on their own.

Referral behavior

Do AI models name Preschool providers?

For service providers the decisive question is whether these systems name anyone at all. Across 45 preschool answers, a specific provider was named in 6.7% of responses on average — roughly 0.2 distinct providers per answer. In practice the assistants behave far more as an explanatory layer than as a referral engine for preschool: visibility comes from being the reasoning a model reproduces, not from being the named recommendation.

The question set

What these 15 Preschool questions cover.

The 15 questions behind every percentage on this page were drawn from real preschool (education services; buyer hiring decisions for this specific service) buyer journeys. Each was put to all 3 models once, with identical wording, so the rates above describe how the assistants handled this exact preschool question set — not a general prior or a hand-picked subset. The full list is shown earlier on this page; the coded percentages are what those specific questions produced.

How to read this

A note on the numbers.

A percentage here is the share of a model's 15 answers in which the behavior appeared at least once — not a confidence score. Because each model answered every question exactly once on 2026-07-04, the figures describe this specific preschool question set and snapshot rather than a general prior. The full protocol and coding rubric are documented in the study methodology.

Methodology

A controlled snapshot, documented end to end.

15 standardized buyer questions per industry, one response per model per question (ChatGPT (gpt-5-mini), Claude (claude-sonnet-5), Gemini (gemini-3-flash-preview)), collected 2026-07-04, coded against a fixed 12-behavior rubric with human QA. AI outputs vary with model version, location and time — figures describe this sample and window, and are refreshed each edition. Read the full methodology →